Seven challenges for image quality research

Image quality assessment has been a topic of recent intense research due to its usefulness in a wide variety of applications. Owing in large part to efforts within the HVEI community, image-quality research has particularly benefited from improved models of visual perception. However, over the last decade, research in image quality has largely shifted from the previous broader objective of gaining a better understanding of human vision, to the current limited objective of better fitting the available ground-truth data. In this paper, we discuss seven open challenges in image quality research. These challenges stem from lack of complete perceptual models for: natural images; suprathreshold distortions; interactions between distortions and images; images containing multiple and nontraditional distortions; and images containing enhancements. We also discuss challenges related to computational efficiency. The objective of this paper is not only to highlight the limitations in our current knowledge of image quality, but to also emphasize the need for additional fundamental research in quality perception.